A Power-Angle-Spectrum Based Clustering and Tracking Algorithm for Time-varying Radio Channels

Radio channel modeling has been an important research topic, since the performance of any communication system depends on channel characteristics. So far, most of existing clustering algorithms are conducted based on the multipath components (MPCs) extracted by using high-resolution parameter estimation approach, e.g., SAGE or MUSIC, etc. However, most of the estimation approaches require prior information to extract MPCs. Moreover, the high-resolution estimation approach usually results in relatively high complexity and thus the clusters can only be identified by using off-line approach after the measurements. Therefore, a power angle spectrum based clustering and tracking algorithm (PASCT) is proposed in this paper. Firstly, power angle spectrum (PAS) is extracted from measurement data by using Bartlett beamformer. For each PAS, potential targets are selected from background and separated into clusters by using image processing approaches. The recognized clusters are characterized into three attributes: i) size, ii) position, and iii) shape feature, where histogram of oriented is developed to describe the shape feature of the clusters. Moreover, a minimizing cost tracking approach based on Kuhn-Munkres method is proposed to accurately identify the clusters in non-stationary channels. The proposed PASCT algorithm is validated based on both simulations and measurements. It is found that the dominating clusters in both line-of-sight and non-line-of-sight environments can be well recognized and tracked with the proposed algorithm. By using the proposed algorithm, the dynamic changes of the clusters in real-time channel measurements, e.g., number, birthdeath process, and size of the clusters, can be well observed. Through the experiments, the proposed can achieve fairly good accuracy on the cluster identification with lower complexity compared to the conventional solution.